Step 1, Download data & connect to GIS:
Data download includes:
mobile_home_percentage_county.zip1950-2018-torn-aspath.zip (Tornado Paths, not points)Note: #2 map in examples above uses income as the social vulnerability in place of mobile home structures. This produces a different thematic map, but similar spatial pattern.
Step 2, Resolve Map Projections:
Check the input data .prj; notice that mobile_home_percentage_county.shp is not in the same projection system as is 1950-2018-torn-aspath.shp. This assignment requires accurate length calculations; both layers in an appropriate projection system is required.
USA Contiguous Lambert Conformal Conic; this will be used for both layers.Map Projections for Input Data
mobile_home_percentage_county.zip. Note that QGIS does not recognize the projection system. To correct, search for USA Contiguous Lambert Conformal Conic in the Project Properties CRS; note that EPSG:102004 is filed under the Authority ID. Select this projection for the Project CRS.Projection Selection
1950-2018-torn-aspath.zip. Reproject and export the layer to the project directory:Reprojection
Step 3, fixed geometries on both layers:
Like assignment 7, both layers need typology fixes in order for sucessful overlay analysis. Run Fix geometries on both layers. Export these layers to the project directory as tornado_paths and mobile_home_counties. These two layers will be the Hazard and Vulnerability inputs to the analysis.
Fix Geometries
tornado_paths cross multiple counties. In order to derive a count of tornadoes per county, there are several overlay techniques that could be utilized. Here we will use Intersection; the result will be a polyline dataset that contains the attributes of the counties in the mobile_home_counties.Layer Overlay
tornado_county.shp:Note: this analysis step may take a minute or two to complete.
Intersection Result
Part III: Table Development:
With the Intersection complete, a summary table will be developed and joined to mobile_home_counties. The field FIPS will be utilized within the Group Stat plugin, gaining a summary count per FIPS county code. This table will then be joined to mobile_home_counties.
First, create a new column one and simply provide a value of 1. This will take a moment to run. When Group Stat calculates a county per FIPS, it will use the 1 value and add up to a total of tornadoes within each FIPS county:
Field Calculator Assignment
FIPS:Group Stat Population
fips_join.csv:Table Export
fips_join.csv into the project via Delimited Text as follows:Load Table
Table Join
Table Join
Note: 222 counties will result as a
NULLjoin - this is expected. These are counties that do not have a tornado record as seen in the image below:
NULL features
As counties vary significantly in size the absolute count of tornadoes per county is not yet comparable. To do so, a constant of 100 sq. miles will be utilized in this step:
First, create geometry attributes (area in square meters) in the Joined layer. This will produce a new layer Added geom info:
Geometry Attributes
sq.miles, type decimal. Divide by 100 to normalize size for a final count of tornadoes per 100 sq miles within each county:(area * 0.00000038610)/100
Areal Normalization
count.100, type whole number:"None" / "sq.miles"
Areal Normalization
Risk Rank
Use the median societal exposure and tornado incidence scores as the break between high and low exposure and incidence - source
NULL values are assumed to be value 0.Note: utilize fields
count.100andMobileHomefor Incidence and Exposure, respectively.
count.100 and MobileHome. Create a new column risk.rank, type whole number and make assignment 1, 2 , 3 and 4 in this new column based on each selection, respectively:Note: populate with value
-999to start. Any records with-999resulting are simply theNULLvalues - counties that have no tornadoes. Those can either be symbolized in the final map as ‘low’ or as ‘No Incidence Records’:
-999 value assigned to table as default value prior to rank assignments 1 through 4
Selection and Assignments for Conditions:
Make sure to clear selections after each condition is assigned in risk.rank and Toogle Off and save each assignment before proceeding to next assignment.
Condition #1 -
"count.100" <3 and "MobileHome" <11risk.rank:Condition 1
Condition #2 -
"count.100" <3 and "MobileHome" >=11risk.rank:Condition 2
Condition #3 -
"count.100" >=3 and "MobileHome" <11risk.rank:Condition 3
Condition #4 -
"count.100" >=3 and "MobileHome" >=11risk.rank:Condition 4
risk.rank field, thematically map and symbolize as follows:Thematic Categorial Map Labels
Thematic Categorial Map Label + Result